Taxi passenger searching strategy recommendation method based on deep learning and big data analysis

Abstract

The invention discloses a taxi passenger searching strategy recommendation method based on deep learning and big data analysis. The recommendation method comprises the following steps of 1, cleaning taxi historical track data; 2, extracting taxi passenger carrying points; 3, extracting taxi passenger carrying hotspots; step 4, carrying out passenger capacity prediction on the hotspot; and 5, proposing a taxi recommendation model. In order to solve the problem of difficulty in finding taxis caused by mismatching of taxis and passenger information in cities, the invention provides a method for predicting the number of future passengers by using deep learning based on traffic big data. According to the technical scheme, a time-varying Markov decision process is used to provide a passenger searching strategy for a taxi driver through strategy iteration, the problem that an existing matching mechanism is difficult to match is solved, the working efficiency of the taxi is improved, the taxiincome is more scientific, and passengers can call the taxi more easily.

Description

technical field [0001] The invention belongs to the field of taxi seeker distribution, and proposes a taxi seeker strategy recommendation method based on deep learning and big data analysis. Background technique [0002] With the continuous improvement of the level of national economic development, motor vehicles account for an increasing proportion of urban traffic, while the corresponding road area per capita has been at a low level, causing huge pressure on urban traffic. In addition, my country's existing urban road network is generally low-density, too large distance between arterial roads, shortage of branch roads, chaotic functions, low-speed traffic system, difficult to meet the needs of modern automobile traffic, modern facilities for traffic control management and traffic safety management Can not meet the actual needs. Congested roads, traffic jams, and chaotic traffic are common in large and medium-sized cities across the country. The rapid growth of the urban p...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to propose a taxi search strategy recommendation method based on deep learning and big data analysis, starting from the perspective of recommending the best passenger search strategy for empty taxis, combining real-time information and historical trajectory data of taxis The recommendation strategy for customer hotspots provides taxi drivers with the best customer-finding solution to solve the problems of difficult matching and low efficiency in the existing matching mechanism

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